# limit the number of cpus used by high performance libraries
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"

import sys
sys.path.insert(0, './yolov5')

from yolov5.models.experimental import attempt_load
from yolov5.utils.downloads import attempt_download
from yolov5.models.common import DetectMultiBackend
from yolov5.utils.datasets import LoadImages, LoadStreams
from yolov5.utils.general import LOGGER, check_img_size, non_max_suppression, scale_coords, check_imshow, xyxy2xywh, \
    increment_path
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.plots import Annotator, colors
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
import argparse
import os
import platform
import shutil
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from yolov5.utils.general import colorstr

#目标检测
def detect(opt):
    out, source, yolo_weights, deep_sort_weights, show_vid, save_vid, save_txt, imgsz, evaluate, half = \
        opt.output, opt.source, opt.yolo_weights, opt.deep_sort_weights, opt.show_vid, opt.save_vid, \
            opt.save_txt, opt.imgsz, opt.evaluate, opt.half
    webcam = source == '0' or source.startswith(
        'rtsp') or source.startswith('http') or source.endswith('.txt')
    # 存放检测结果的文件夹
    # runs/detect/exp{number}
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
    # initialize deepsort 初始化deepsort 参数
    cfg = get_config()
    cfg.merge_from_file(opt.config_deepsort)
    #下载deepsort权重 默认为deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7
    attempt_download(deep_sort_weights, repo='mikel-brostrom/Yolov5_DeepSort_Pytorch')
    deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
                        max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
                        max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
                        max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
                        use_cuda=True)

    # Initialize 设备
    device = select_device(opt.device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA

    # The MOT16 evaluation runs multiple inference streams in parallel, each one writing to
    # its own .txt file. Hence, in that case, the output folder is not restored
    #评估若存在就删除之前的文件
    if not evaluate:
        if os.path.exists(out):
            pass
            shutil.rmtree(out)  # delete output folder
        os.makedirs(out)  # make new output folder

    # Load model 加载模型
    device = select_device(device)
    #yolo5 模型加载
    model = DetectMultiBackend(opt.yolo_weights, device=device, dnn=opt.dnn)
    #将模型的stride赋给stride变量 32
    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Half
    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
    if pt:
        model.model.half() if half else model.model.float()

    # Set Dataloader
    vid_path, vid_writer = None, None
    # Check if environment supports image displays
    if show_vid:
        show_vid = check_imshow()

    # Dataloader 如果是文件流LoadStreams 其他LoadImages
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Get names and colors
    # 载入模型的识别物体名称
    names = model.module.names if hasattr(model, 'module') else model.names

    #输出路径
    save_path = str(Path(out))
    print("save_path",out,save_path)
    # extract what is in between the last '/' and last '.'
    #txt_file_name = source.split('/')[-1].split('.')[0]
    source = source.replace('\\', '/')
    txt_file_name = source.split('/')[-1].split('.')[0]
    print("source ",source,txt_file_name)
    txt_path = str(Path(out)) + '/' + txt_file_name + '.txt'

    if pt and device.type != 'cpu':
        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    #path 图片路径
    #img resize后的图片，形状为[3, 480, 640]，后面两个维度有一个维度为640，另外一个是按比例调整后的大小
    #img0 是原始图像 形状为w h 3
    for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset):
        t1 = time_sync()
        #将img转成张量
        img = torch.from_numpy(img).to(device)
        #cpu还是用32位浮点数
        img = img.half() if half else img.float()  # uint8 to fp16/32
        #将图片张量内数据归一化
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        # 将图片张量转换成[batch, 3, w, h]的形式
        if img.ndimension() == 3:
            #增加维度
            img = img.unsqueeze(0)
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference 可视化
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if opt.visualize else False

        #预测
        # 前向传播后的输出结果，具体请看核心函数
        # 最后输出结果为[batch, n, 85]，我这里batch为1， n为18900
        pred = model(img, augment=opt.augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        # Apply NMS 非最大值抑制
        # 非极大抑制算法，通过此函数确定图片中物体的类别和坐标
        # 本例中，从图片里找到了两个物体，因此返回结果的形状为[2, 6]
        # 值如下
        # tensor([[3.75013e+02, 4.61683e+01, 6.35028e+02, 4.73212e+02, 5.46111e-01, 0.00000e+00],
        #        [5.54439e+02, 3.57350e+02, 5.99121e+02, 4.65176e+02, 4.34204e-01, 2.70000e+01]])
        # 前4个值为坐标，第5个值为物体置信度，第6个值为物体的类别
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms, max_det=opt.max_det)
        dt[2] += time_sync() - t3

        # Process detections 检测
        # 遍历预测边框置信度等值进行 deepsort计算
        for i, det in enumerate(pred):  # detections per image
            # 记录帧数
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            s += '%gx%g ' % img.shape[2:]  # print string
            save_path = str(Path(out) / Path(p).name)

            annotator = Annotator(im0, line_width=2, pil=not ascii)

            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # 把输出坐标转换成 xywh 置信度 分类id
                #取二维矩阵的前四个元素生成一个新的矩阵 然后计算坐标宽度
                #[[375.013   46.1683 635.028  473.212 ]
                #[554.439  357.35   599.121  465.176 ]]
                xywhs = xyxy2xywh(det[:, 0:4])
                # 取第5 个元素 组成一个新的矩阵 [0.546111 0.434204]
                confs = det[:, 4]
                clss = det[:, 5]

                # pass detections to deepsort 检测结果更新deepsort
                outputs = deepsort.update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0)
                
                # draw boxes for visualization
                #x1, y1, x2, y2, track_id, class_id
                if len(outputs) > 0:
                    for j, (output, conf) in enumerate(zip(outputs, confs)): 
                        
                        bboxes = output[0:4]
                        id = output[4]
                        cls = output[5]

                        c = int(cls)  # integer class
                        label = f'{id} {names[c]} {conf:.2f}'
                        annotator.box_label(bboxes, label, color=colors(c, True))

                        if save_txt:
                            # to MOT format
                            bbox_left = output[0]
                            bbox_top = output[1]
                            bbox_w = output[2] - output[0]
                            bbox_h = output[3] - output[1]
                            # Write MOT compliant results to file
                            with open(txt_path, 'a') as f:
                               f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left,
                                                           bbox_top, bbox_w, bbox_h, -1, -1, -1, -1))  # label format

            else:
                deepsort.increment_ages()

            # Print time (inference-only)
            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

            # Stream results
            im0 = annotator.result()
            if show_vid:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_vid:
                if vid_path != save_path:  # new video
                    vid_path = save_path
                    if isinstance(vid_writer, cv2.VideoWriter):
                        vid_writer.release()  # release previous video writer
                    if vid_cap:  # video
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    else:  # stream
                        fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path += '.mp4'

                    vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                vid_writer.write(im0)

    # Print results
    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
    if save_txt or save_vid:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
        if platform == 'darwin':  # MacOS
            os.system('open ' + save_path)


if __name__ == '__main__':
    #参数说明
    parser = argparse.ArgumentParser()
    # 指定文件权重yolo权重和deepsort权重
    parser.add_argument('--yolo_weights', nargs='+', type=str, default='yolov5l.pt', help='model.pt path(s)')
    parser.add_argument('--deep_sort_weights', type=str, default='deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7', help='ckpt.t7 path')
    # file/folder, 0 for webcam
    #指定文件来源
    parser.add_argument('--source', type=str, default='0', help='source')
    #指定输出文件
    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
    #检测图像大小
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    #置信度阈值
    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
    #iou阈值进行nms的计算
    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
    #抽帧设置
    parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    #展示设置以视频的方式展示
    parser.add_argument('--show-vid', action='store_true', help='display tracking video results')
    #结果保存以视频的方式保存或文本保存
    parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
    parser.add_argument('--save-txt', action='store_true', help='save MOT compliant results to *.txt')
    # class 0 is person, 1 is bycicle, 2 is car... 79 is oven 识别物体设置
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 16 17')
    #目标检测线性回归
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    #augment
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    #评估
    parser.add_argument('--evaluate', action='store_true', help='augmented inference')
    #deepsort配置
    parser.add_argument("--config_deepsort", type=str, default="deep_sort_pytorch/configs/deep_sort.yaml")
    #半精度计算
    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
    #可视化特征
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    #每张图检测的最大数量
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detection per image')
    #使用dnn进行预测
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    parser.add_argument('--project', default='', help='project')
    parser.add_argument('--name', default='', help='name')
    parser.add_argument('--exist_ok', default='True', help='exist_ok')
    opt = parser.parse_args()
    #初始化图片尺寸
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand

    #反向传播关闭
    with torch.no_grad():
        detect(opt)
